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基于matlab，利用背景减除法实现运动目标的跟踪。本代码所用的函数均适用于新版本的matlab，不需要额外修改。在MATLAB2014a编译通过。(Based on matlab, for tracking moving objects using background subtraction method. The code used functions are applied to the new version of matlab, no additional modifications. In MATLAB2014a compile.)

DUFFING方程的混沌时序相空间重构程序(DUFFING equation phase space reconstruction of chaotic time series procedures)

说明：  阵列信号处理的基本doa算法，matlab下运行，非常有用(Array signal processing basic doa algorithm, matlab running, very useful)

MATLAB GUI学习例子，包含时钟模拟例子等，对GUI学习者有帮助。(MATLAB GUI study examples, including examples of clock simulation, the GUI learners helpful.)

说明：  matlab BP算法例程（神经网络） 独自首创BP_Matlab.rar(Matlab routines (neural networks) alone in first BP_Matlab.rar)

MATLAB编程时用到的函数参考介绍及功能描述。(Function used in the MATLAB Programming Reference introduction and functional description.)

allencahn方程的matlab实现，附图(allencahn in matlab code)

lpc语音信号合成，给出了完整的LPC matlab程序，包括语音信号采集，LP预测系数(failed to translate)

一篇基于Matlab到OFDM研究开发的论文，有需要的朋友可以下载看看(An OFDM-based research and development of Matlab to the papers, a friend in need can be downloaded to see)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)